OPTIMIZATION DECISION ON INFORMAL RECYCLING CHANNEL OF ELECTRIC VECHICLE BATTERIES AND SUPERVISION STRATEGY

In order to explore the impact of the government’ regulatory strategy on the informal recycling channel of electric vehicle batteries, this paper compares and analyzes the evolutionary strategy of the government and the informal recycling channel of electric vehicle batteries group under fixed and dynamic punishment model. The results show that: (1) the dynamic system of replication under the fixed punishment model consists of four saddle points and one central point. The evolution process of the strategy is repetitive and periodic, and cannot reach equilibrium. (2) The dynamic system of replication under the dynamic punishment model consists of four saddle points and one focal point, and the stable Nash equilibrium can be achieved. That is to say, the optimized scenario is valid. (3) In order to improve the probability of the informal recycling channel of electric vehicle batteries choosing the transformation upgrading strategy, improving the legal environmental incomes after the transformation upgrading of the informal recycling channel of electric vehicle batteries group, and reducing the recycling cost of the available components extracted from the waste electric vehicle batteries through formal channels are effective approaches.

[1]  Margaret Rolfe,et al.  Perceptions of risk from nanotechnologies and trust in stakeholders: a cross sectional study of public, academic, government and business attitudes , 2015, BMC Public Health.

[2]  H. Thomas,et al.  A review of processes and technologies for the recycling of lithium-ion secondary batteries , 2008 .

[3]  J. M. Turner,et al.  Charging up Battery Recycling Policies: Extended Producer Responsibility for Single‐Use Batteries in the European Union, Canada, and the United States , 2016 .

[4]  Zhixue Liu,et al.  Optimal electric vehicle production strategy under subsidy and battery recycling , 2017 .

[5]  Xiangyun Chang,et al.  Impact of subsidy policies on recycling and remanufacturing using system dynamics methodology: a case of auto parts in China , 2014 .

[6]  Luk N. Van Wassenhove,et al.  Reverse Channel Design: The Case of Competing Retailers , 2006, Manag. Sci..

[7]  S. Liu,et al.  MODELLING AND SIMULATION ON RECYCLING OF ELECTRIC VEHICLE BATTERIES – USING AGENT APPROACH , 2014 .

[8]  D. Friedman.  On economic applications of evolutionary game theory , 1998 .

[9]  F. Qiao,et al.  ESTIMATION OF VEHICLE EMISSION ON MAINLINE FREEWAY UNDER ISOLATED AND INTEGRATED RAMP METERING STRATEGIES , 2018 .

[10]  Feng Wu,et al.  Recovery of valuable metals from spent lithium-ion batteries by ultrasonic-assisted leaching process , 2014 .

[11]  Yufeng Wu,et al.  An overview of recycling and treatment of spent LiFePO4 batteries in China , 2017 .

[12]  Luk N. Van Wassenhove,et al.  Closed - Loop Supply Chain Models with Product Remanufacturing , 2004, Manag. Sci..

[13]  Fuquan Zhao,et al.  Impact of recycling on energy consumption and greenhouse gas emissions from electric vehicle production: The China 2025 case , 2017 .

[14]  D. Gong,et al.  Achieving sustainable transport through resource scheduling: A case study for electric vehicle charging stations , 2019, Advances in Production Engineering & Management.

[15]  I-Hsuan Hong,et al.  Modeling closed-loop supply chains in the electronics industry: A retailer collection application , 2012 .

[16]  Mingli Zhang,et al.  What keeps Chinese from recycling: Accessibility of recycling facilities and the behavior , 2016 .

[17]  Aymeric Girard,et al.  Processes and technologies for the recycling and recovery of spent lithium-ion batteries , 2016 .

[18]  Jinhui Li,et al.  Recycling metals from wastes: a novel application of mechanochemistry. , 2015, Environmental science & technology.

[19]  D. Friedman.  EVOLUTIONARY GAMES IN ECONOMICS , 1991 .

[20]  Georges Zaccour,et al.  A two-period game of a closed-loop supply chain , 2012, Eur. J. Oper. Res..